Your first neural network

In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and the model more.

In [1]:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

Load and prepare the data

A critical step in working with neural networks is preparing the data correctly. Variables on different scales make it difficult for the network to efficiently learn the correct weights. Below, we've written the code to load and prepare the data. You'll learn more about this soon!

In [2]:
data_path = 'Bike-Sharing-Dataset/hour.csv'

rides = pd.read_csv(data_path)
In [3]:
rides.head()
Out[3]:
instant dteday season yr mnth hr holiday weekday workingday weathersit temp atemp hum windspeed casual registered cnt
0 1 2011-01-01 1 0 1 0 0 6 0 1 0.24 0.2879 0.81 0.0 3 13 16
1 2 2011-01-01 1 0 1 1 0 6 0 1 0.22 0.2727 0.80 0.0 8 32 40
2 3 2011-01-01 1 0 1 2 0 6 0 1 0.22 0.2727 0.80 0.0 5 27 32
3 4 2011-01-01 1 0 1 3 0 6 0 1 0.24 0.2879 0.75 0.0 3 10 13
4 5 2011-01-01 1 0 1 4 0 6 0 1 0.24 0.2879 0.75 0.0 0 1 1

Checking out the data

This dataset has the number of riders for each hour of each day from January 1 2011 to December 31 2012. The number of riders is split between casual and registered, summed up in the cnt column. You can see the first few rows of the data above.

Below is a plot showing the number of bike riders over the first 10 days or so in the data set. (Some days don't have exactly 24 entries in the data set, so it's not exactly 10 days.) You can see the hourly rentals here. This data is pretty complicated! The weekends have lower over all ridership and there are spikes when people are biking to and from work during the week. Looking at the data above, we also have information about temperature, humidity, and windspeed, all of these likely affecting the number of riders. You'll be trying to capture all this with your model.

In [4]:
rides[:24*10].plot(x='dteday', y='cnt')
Out[4]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f8467ef3f60>

Dummy variables

Here we have some categorical variables like season, weather, month. To include these in our model, we'll need to make binary dummy variables. This is simple to do with Pandas thanks to get_dummies().

In [5]:
dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']
for each in dummy_fields:
    dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)
    rides = pd.concat([rides, dummies], axis=1)

fields_to_drop = ['instant', 'dteday', 'season', 'weathersit', 
                  'weekday', 'atemp', 'mnth', 'workingday', 'hr']
data = rides.drop(fields_to_drop, axis=1)
data.head()
Out[5]:
yr holiday temp hum windspeed casual registered cnt season_1 season_2 ... hr_21 hr_22 hr_23 weekday_0 weekday_1 weekday_2 weekday_3 weekday_4 weekday_5 weekday_6
0 0 0 0.24 0.81 0.0 3 13 16 1 0 ... 0 0 0 0 0 0 0 0 0 1
1 0 0 0.22 0.80 0.0 8 32 40 1 0 ... 0 0 0 0 0 0 0 0 0 1
2 0 0 0.22 0.80 0.0 5 27 32 1 0 ... 0 0 0 0 0 0 0 0 0 1
3 0 0 0.24 0.75 0.0 3 10 13 1 0 ... 0 0 0 0 0 0 0 0 0 1
4 0 0 0.24 0.75 0.0 0 1 1 1 0 ... 0 0 0 0 0 0 0 0 0 1

5 rows × 59 columns

Scaling target variables

To make training the network easier, we'll standardize each of the continuous variables. That is, we'll shift and scale the variables such that they have zero mean and a standard deviation of 1.

The scaling factors are saved so we can go backwards when we use the network for predictions.

In [6]:
quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']
# Store scalings in a dictionary so we can convert back later
scaled_features = {}
for each in quant_features:
    mean, std = data[each].mean(), data[each].std()
    scaled_features[each] = [mean, std]
    data.loc[:, each] = (data[each] - mean)/std

Splitting the data into training, testing, and validation sets

We'll save the data for the last approximately 21 days to use as a test set after we've trained the network. We'll use this set to make predictions and compare them with the actual number of riders.

In [7]:
# Save data for approximately the last 21 days 
test_data = data[-21*24:]

# Now remove the test data from the data set 
data = data[:-21*24]

# Separate the data into features and targets
target_fields = ['cnt', 'casual', 'registered']
features, targets = data.drop(target_fields, axis=1), data[target_fields]
test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]
print(targets[:15])

         cnt    casual  registered
0  -0.956312 -0.662736   -0.930162
1  -0.823998 -0.561326   -0.804632
2  -0.868103 -0.622172   -0.837666
3  -0.972851 -0.662736   -0.949983
4  -1.039008 -0.723582   -1.009445
5  -1.039008 -0.723582   -1.009445
6  -1.033495 -0.683018   -1.016052
7  -1.027981 -0.703300   -1.002838
8  -1.000416 -0.703300   -0.969804
9  -0.967338 -0.561326   -0.976411
10 -0.846051 -0.480199   -0.857487
11 -0.735789 -0.196252   -0.817845
12 -0.581424 -0.135406   -0.652673
13 -0.526293  0.229668   -0.705528
14 -0.460137 -0.013715   -0.546963

We'll split the data into two sets, one for training and one for validating as the network is being trained. Since this is time series data, we'll train on historical data, then try to predict on future data (the validation set).

In [8]:
# Hold out the last 60 days or so of the remaining data as a validation set
train_features, train_targets = features[:-60*24], targets[:-60*24]
val_features, val_targets = features[-60*24:], targets[-60*24:]

Time to build the network

Below you'll build your network. We've built out the structure and the backwards pass. You'll implement the forward pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.

The network has two layers, a hidden layer and an output layer. The hidden layer will use the sigmoid function for activations. The output layer has only one node and is used for the regression, the output of the node is the same as the input of the node. That is, the activation function is $f(x)=x$. A function that takes the input signal and generates an output signal, but takes into account the threshold, is called an activation function. We work through each layer of our network calculating the outputs for each neuron. All of the outputs from one layer become inputs to the neurons on the next layer. This process is called forward propagation.

We use the weights to propagate signals forward from the input to the output layers in a neural network. We use the weights to also propagate error backwards from the output back into the network to update our weights. This is called backpropagation.

Hint: You'll need the derivative of the output activation function ($f(x) = x$) for the backpropagation implementation. If you aren't familiar with calculus, this function is equivalent to the equation $y = x$. What is the slope of that equation? That is the derivative of $f(x)$.

Below, you have these tasks:

  1. Implement the sigmoid function to use as the activation function. Set self.activation_function in __init__ to your sigmoid function.
  2. Implement the forward pass in the train method.
  3. Implement the backpropagation algorithm in the train method, including calculating the output error.
  4. Implement the forward pass in the run method.
In [174]:
class NeuralNetwork(object):
    def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):
        # Set number of nodes in input, hidden and output layers.
        self.input_nodes = input_nodes     #3
        self.hidden_nodes = hidden_nodes   #2
        self.output_nodes = output_nodes   #1

        # Initialize weights
        # w_input_hidden  (3,2)
        self.weights_input_to_hidden = np.random.normal(0.0, self.input_nodes**-0.5, 
                                       (self.input_nodes, self.hidden_nodes))
        # w_hidden_output (2,1)
        self.weights_hidden_to_output = np.random.normal(0.0, self.hidden_nodes**-0.5, 
                                       (self.hidden_nodes,output_nodes))
       
        self.lr = learning_rate  # 0.5
        
        #### TODO: Set self.activation_function to your implemented sigmoid function ####
        self.activation_function = lambda x : (1 / (1 + np.exp(-x)))  # Replace 0 with your sigmoid calculation.
        self.sigmoid_prime = lambda x : x * (1 - x)  # sigmoid_prime
        
    def train(self, features, targets):
        ''' Train the network on batch of features and targets. 
            features: 2D array, each row is one data record, each column is a feature
            targets: 1D array of target values
        '''
        #inputs = np.array([[0.5, -0.2, 0.1]])
        #targets = np.array([[0.4]])
        # features : (1,3), targets : (1,1)
        
        n_records = features.shape[0]
        
        # zero matrix
        # (3,2)
        delta_weights_i_h = np.zeros(self.weights_input_to_hidden.shape)
        # (2,1)
        delta_weights_h_o = np.zeros(self.weights_hidden_to_output.shape)
        
        for X, y in zip(features, targets):
            # features : (1,3), targets : (1,1)
            #  X (3,)   X[:,None]    y (1,)  -vector shape  []
            
            ######## Forward pass ########
            # TODO: Hidden layer - Replace these values with your calculations.
            #  (2,)     [-0.06 -0.18]
            hidden_inputs = np.dot(X, self.weights_input_to_hidden) # signals into hidden layer
             
            #  (2,) [ 0.4850045   0.45512111]
            hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer
            

            
            # TODO: Output layer - Replace these values with your calculations.
            final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output)  # signals into final output layer

            #  (1,) [ 0.09998924]
            final_outputs = final_inputs # signals from final output layer
               
            
            
            #### Implement the backward pass here ####
            ### Backward pass ###

            # TODO: Output error - Replace this value with your calculations.
            
            # (1, )  [ 0.30001076]
            error = y - final_outputs # Output layer error is the difference between desired target and actual output.
           
            
            # (1,)   [ 0.30001076]
            #output_error_term = error * self.sigmoid_prime(final_outputs) 
            output_error_term = error * 1
            
            
            #  self.weights_hidden_to_output
            #  (2, 1)  -  use T to convert row to column  (1,2)
            #  hidden_error
            #  (2,) [ 0.09000323 -0.03000108]
            hidden_error = np.dot(output_error_term , self.weights_hidden_to_output.T)

            # (2,)  [ 0.02248057 -0.00743984]
            hidden_error_term = hidden_error * self.sigmoid_prime(hidden_outputs)
            
            
            # Weight step (hidden to output)
            print(output_error_term) 
            print(hidden_outputs)
            print (X)
            print(hidden_outputs.shape)
            print(hidden_outputs[:None])
            print(hidden_outputs[:None].shape)
            delta_weights_h_o +=  output_error_term * hidden_outputs[:,None]
           
            # Weight step (input to hidden)
            delta_weights_i_h += hidden_error_term * X[:,None]
            

        # TODO: Update the weights - Replace these values with your calculations.
        self.weights_hidden_to_output += self.lr * delta_weights_h_o/n_records # update hidden-to-output weights with gradient descent step
        self.weights_input_to_hidden += self.lr * delta_weights_i_h/n_records # update input-to-hidden weights with gradient descent step
 
    def run(self, features):
        ''' Run a forward pass through the network with input features 
            features: 1D array of feature values
        '''
        
        #### Implement the forward pass here ####
        # TODO: Hidden layer - replace these values with the appropriate calculations.
        hidden_inputs = np.dot(features,self.weights_input_to_hidden ) # signals into hidden layer
        hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer
        
        # TODO: Output layer - Replace these values with the appropriate calculations.
        final_inputs = np.dot(hidden_outputs,self.weights_hidden_to_output) # signals into final output layer
        final_outputs = final_inputs # signals from final output layer 
        # (1, 1)  [[0.09998924]]   vector
        
        return final_outputs
In [175]:
def MSE(y, Y):
    return np.mean((y-Y)**2)

Unit tests

Run these unit tests to check the correctness of your network implementation. This will help you be sure your network was implemented correctly befor you starting trying to train it. These tests must all be successful to pass the project.

In [176]:
import unittest

inputs = np.array([[0.5, -0.2, 0.1]])
targets = np.array([[0.4]])
# test_w_i_h (3,2)
test_w_i_h = np.array([[0.1, -0.2],
                       [0.4, 0.5],
                       [-0.3, 0.2]])

test_w_h_o = np.array([[0.3],
                       [-0.1]])

class TestMethods(unittest.TestCase):
    
    ##########
    # Unit tests for data loading
    ##########
    
    def test_data_path(self):
        # Test that file path to dataset has been unaltered
        self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')
        
    def test_data_loaded(self):
        # Test that data frame loaded
        self.assertTrue(isinstance(rides, pd.DataFrame))
    
    ##########
    # Unit tests for network functionality
    ##########

    def test_activation(self):
        network = NeuralNetwork(3, 2, 1, 0.5)
        # Test that the activation function is a sigmoid
        self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))

    def test_train(self):
        # Test that weights are updated correctly on training
        network = NeuralNetwork(3, 2, 1, 0.5)
        network.weights_input_to_hidden = test_w_i_h.copy()
        network.weights_hidden_to_output = test_w_h_o.copy()
        
        network.train(inputs, targets)
        self.assertTrue(np.allclose(network.weights_hidden_to_output, 
                                    np.array([[ 0.37275328], 
                                              [-0.03172939]])))
        self.assertTrue(np.allclose(network.weights_input_to_hidden,
                                    np.array([[ 0.10562014, -0.20185996], 
                                              [0.39775194, 0.50074398], 
                                              [-0.29887597, 0.19962801]])))

    def test_run(self):
        # Test correctness of run method
        network = NeuralNetwork(3, 2, 1, 0.5)
        network.weights_input_to_hidden = test_w_i_h.copy()
        network.weights_hidden_to_output = test_w_h_o.copy()

        self.assertTrue(np.allclose(network.run(inputs), 0.09998924))

suite = unittest.TestLoader().loadTestsFromModule(TestMethods())
unittest.TextTestRunner().run(suite)

.....
[ 0.30001076]
[ 0.4850045   0.45512111]
[ 0.5 -0.2  0.1]
(2,)
[ 0.4850045   0.45512111]
(2,)

----------------------------------------------------------------------
Ran 5 tests in 0.010s

OK
Out[176]:
<unittest.runner.TextTestResult run=5 errors=0 failures=0>

Training the network

Here you'll set the hyperparameters for the network. The strategy here is to find hyperparameters such that the error on the training set is low, but you're not overfitting to the data. If you train the network too long or have too many hidden nodes, it can become overly specific to the training set and will fail to generalize to the validation set. That is, the loss on the validation set will start increasing as the training set loss drops.

You'll also be using a method know as Stochastic Gradient Descent (SGD) to train the network. The idea is that for each training pass, you grab a random sample of the data instead of using the whole data set. You use many more training passes than with normal gradient descent, but each pass is much faster. This ends up training the network more efficiently. You'll learn more about SGD later.

Choose the number of iterations

This is the number of batches of samples from the training data we'll use to train the network. The more iterations you use, the better the model will fit the data. However, if you use too many iterations, then the model with not generalize well to other data, this is called overfitting. You want to find a number here where the network has a low training loss, and the validation loss is at a minimum. As you start overfitting, you'll see the training loss continue to decrease while the validation loss starts to increase.

Choose the learning rate

This scales the size of weight updates. If this is too big, the weights tend to explode and the network fails to fit the data. A good choice to start at is 0.1. If the network has problems fitting the data, try reducing the learning rate. Note that the lower the learning rate, the smaller the steps are in the weight updates and the longer it takes for the neural network to converge.

Choose the number of hidden nodes

The more hidden nodes you have, the more accurate predictions the model will make. Try a few different numbers and see how it affects the performance. You can look at the losses dictionary for a metric of the network performance. If the number of hidden units is too low, then the model won't have enough space to learn and if it is too high there are too many options for the direction that the learning can take. The trick here is to find the right balance in number of hidden units you choose.

In [177]:
import sys

### Set the hyperparameters here ###
iterations = 100
learning_rate = 0.1
hidden_nodes = 2
output_nodes = 1

N_i = train_features.shape[1]
network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)

losses = {'train':[], 'validation':[]}
for ii in range(iterations):
    # Go through a random batch of 128 records from the training data set
    batch = np.random.choice(train_features.index, size=128)
    X, y = train_features.ix[batch].values, train_targets.ix[batch]['cnt']
                             
    network.train(X, y)
    
    # Printing out the training progress
    train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)
    val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)
    sys.stdout.write("\rProgress: {:2.1f}".format(100 * ii/float(iterations)) \
                     + "% ... Training loss: " + str(train_loss)[:5] \
                     + " ... Validation loss: " + str(val_loss)[:5])
    sys.stdout.flush()
    
    losses['train'].append(train_loss)
    losses['validation'].append(val_loss)

[ 0.00413526]
[ 0.48508527  0.53588166]
[ 0.          0.          0.95043892 -0.08930106  0.27629827  0.          0.
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(2,)
[ 0.48508527  0.53588166]
(2,)
[-0.10658311]
[ 0.43497125  0.5356502 ]
[ 0.          0.          1.05430474  1.10284219  1.86203995  0.          0.
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[ 0.43497125  0.5356502 ]
(2,)
[-0.67818662]
[ 0.39425021  0.37462551]
[ 1.          0.         -1.75007248 -0.81495348  0.88607317  1.          0.
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[ 0.39425021  0.37462551]
(2,)
[ 2.62223993]
[ 0.4063613   0.47275761]
[ 1.          0.          0.74270727 -1.38510895  0.51988127  0.          0.
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(2,)
[ 0.4063613   0.47275761]
(2,)
[-0.96679539]
[ 0.4666506   0.65144085]
[ 0.          0.          0.53497563  1.31017145  0.03189788  0.          0.
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[ 0.4666506   0.65144085]
(2,)
[-0.60023341]
[ 0.56061854  0.47725139]
[ 0.          0.         -0.81528007  1.56933303  1.37405656  0.          1.
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[ 0.56061854  0.47725139]
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[ 1.550224]
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[ 1.          0.          0.22337816  0.06619588  0.51988127  0.          1.
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(2,)
[ 0.37594763  0.58258008]
(2,)
[-0.10422996]
[ 0.4768128   0.53890883]
[ 1.          0.          0.8465731   1.36200377  0.51988127  0.          0.
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[ 0.4768128   0.53890883]
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[-0.09582184]
[ 0.58988611  0.53669338]
[ 1.          0.         -0.60754843  0.68818367  0.88607317  1.          0.
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(2,)
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(2,)
[ 2.04034341]
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[ 0.46018141  0.66568756]
(2,)
[ 0.49999892]
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[ 0.46858765  0.47289974]
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[ 0.33100603  0.45120091]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[-0.62578316]
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(2,)
[ 0.4727482   0.41456992]
(2,)
[-0.91338021]
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(2,)
[ 0.47632084  0.58358221]
(2,)
Progress: 0.0% ... Training loss: 0.944 ... Validation loss: 1.445[ 1.12108585]
[ 0.53610266  0.48662013]
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(2,)
[ 0.53610266  0.48662013]
(2,)
[ 2.76103758]
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[ 0.48629361  0.7459109 ]
(2,)
[-0.97829699]
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(2,)
[ 0.42175094  0.60320703]
(2,)
[ 4.31251048]
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(2,)
[ 0.46354528  0.68853924]
(2,)
[ 1.25609495]
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(2,)
[ 0.39107691  0.58212449]
(2,)
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(2,)
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(2,)
[-0.91980669]
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(2,)
[ 0.48198462  0.53816868]
(2,)
[ 0.4662555]
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(2,)
[ 0.31575749  0.5644396 ]
(2,)
[-0.18425651]
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[ 0.49029078  0.47141482]
(2,)
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[ 0.47114658  0.58274353]
(2,)
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(2,)
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[ 0.50181892  0.37839777]
(2,)
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[ 0.3639156   0.54499733]
(2,)
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[ 0.39602136  0.56777085]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
Progress: 1.0% ... Training loss: 0.935 ... Validation loss: 1.434[ 1.12561237]
[ 0.36896433  0.52298261]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.50335913  0.36908565]
(2,)
[ 1.69103765]
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(2,)
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[ 0.46382243  0.49356262]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.44495616  0.5241003 ]
(2,)
[ 1.19511747]
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(2,)
[ 0.51760929  0.69160016]
(2,)
[-0.14136626]
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(2,)
[ 0.55064855  0.44831327]
(2,)
Progress: 2.0% ... Training loss: 0.930 ... Validation loss: 1.425[-0.18022525]
[ 0.46787323  0.61894793]
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(2,)
[ 3.72260528]
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(2,)
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(2,)
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(2,)
[ 0.48767655  0.59132196]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.47493253  0.40586681]
(2,)
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[ 0.42951393  0.52820342]
(2,)
[ 2.09871987]
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[ 0.28033015  0.5617661 ]
(2,)
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(2,)
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(2,)
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[ 0.4450919   0.58660631]
(2,)
[ 1.59863199]
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[ 0.35608029  0.61456068]
(2,)
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[ 0.46374534  0.59079324]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.41953356  0.55096741]
(2,)
[ 0.72998794]
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(2,)
[ 0.64754196  0.64208872]
(2,)
Progress: 3.0% ... Training loss: 0.924 ... Validation loss: 1.420[ 0.56454982]
[ 0.36630584  0.53890365]
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(2,)
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[ 0.35671069  0.64096375]
(2,)
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(2,)
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[ 0.44904144  0.56519393]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.52825133  0.5384883 ]
(2,)
[ 2.59581949]
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(2,)
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(2,)
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(2,)
[ 0.35653049]
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(2,)
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(2,)
[ 0.45090582  0.43375007]
(2,)
[-0.42896315]
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[ 0.39678521  0.60291704]
(2,)
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[ 0.42268516  0.52684712]
(2,)
[ 2.08876096]
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[ 0.28687094  0.61021313]
(2,)
[-1.06372195]
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(2,)
[ 0.39438486  0.68222408]
(2,)
[-0.8094565]
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(2,)
[ 0.43808828  0.43621167]
(2,)
Progress: 4.0% ... Training loss: 0.919 ... Validation loss: 1.414[-0.1111935]
[ 0.36885507  0.57882386]
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(2,)
[ 0.36885507  0.57882386]
(2,)
[-0.27835162]
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[ 0.50972674  0.37018973]
(2,)
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[ 0.42122681  0.58823589]
(2,)
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(2,)
[ 0.59503872  0.58475977]
(2,)
[-0.44972976]
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(2,)
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(2,)
[-0.71826904]
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(2,)
[ 0.4388144   0.50036334]
(2,)
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(2,)
[ 0.42484616  0.61584508]
(2,)
[ 1.34840848]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.11579266]
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.59714242  0.5143011 ]
(2,)
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(2,)
[ 0.56871211  0.38962871]
(2,)
Progress: 5.0% ... Training loss: 0.913 ... Validation loss: 1.404[ 1.17038688]
[ 0.42100155  0.43219741]
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(2,)
[ 0.42100155  0.43219741]
(2,)
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(2,)
[ 0.51051745  0.58262357]
(2,)
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(2,)
[ 0.43265307  0.4331635 ]
(2,)
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(2,)
[ 0.58375885  0.54339329]
(2,)
[-1.12306444]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[-0.17856195]
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(2,)
[ 0.43032011  0.65442959]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.52655066  0.63500875]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
Progress: 6.0% ... Training loss: 0.907 ... Validation loss: 1.397[-0.36692666]
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.25923968]
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(2,)
[ 0.31365893  0.57746103]
(2,)
Progress: 7.0% ... Training loss: 0.904 ... Validation loss: 1.395[ 0.40876081]
[ 0.3667465   0.61482995]
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(2,)
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(2,)
[-0.12747748]
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(2,)
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(2,)
[-0.40093066]
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(2,)
[ 0.53281387  0.40661038]
(2,)
Progress: 8.0% ... Training loss: 0.901 ... Validation loss: 1.392[ 2.56444273]
[ 0.26171745  0.59082278]
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.40836733  0.52522539]
(2,)
[ 1.51314339]
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(2,)
[ 0.39488327  0.56312088]
(2,)
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(2,)
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(2,)
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(2,)
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[ 0.38222623  0.60774102]
(2,)
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(2,)
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(2,)
[-0.80097]
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(2,)
[ 0.48883297  0.58622144]
(2,)
[ 0.74266606]
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[ 0.36523056  0.5635669 ]
(2,)
Progress: 9.0% ... Training loss: 0.895 ... Validation loss: 1.383[-0.5367742]
[ 0.42707867  0.47413165]
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(2,)
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(2,)
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[ 0.49273392  0.4166079 ]
(2,)
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[ 0.44312795  0.57708685]
(2,)
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(2,)
[ 3.23617218]
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[ 0.46265867  0.68175321]
(2,)
[-1.06699208]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
Progress: 10.0% ... Training loss: 0.892 ... Validation loss: 1.385[-0.41107561]
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(2,)
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[ 0.65857784  0.37306318]
(2,)
[ 0.67339064]
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[ 0.48776298  0.61132948]
(2,)
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[ 0.44399827  0.4744451 ]
(2,)
[ 0.19790505]
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[ 0.31034412  0.61122514]
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[ 0.58220661  0.4748298 ]
(2,)
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[ 0.45961336  0.53270209]
(2,)
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[ 0.44317226  0.53354467]
(2,)
[-0.69136544]
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(2,)
[ 0.43066    0.5292359]
(2,)
[-0.77917533]
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(2,)
[ 0.46299174  0.6073036 ]
(2,)
Progress: 11.0% ... Training loss: 0.887 ... Validation loss: 1.379[-0.6502669]
[ 0.36111238  0.63877758]
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(2,)
[ 0.36111238  0.63877758]
(2,)
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[ 0.46049235  0.63558071]
(2,)
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(2,)
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(2,)
[ 0.39038969  0.57460893]
(2,)
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[ 0.43668492  0.68649763]
(2,)
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(2,)
[ 0.5280009   0.40241208]
(2,)
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[ 0.42793029  0.49187804]
(2,)
[-0.56333565]
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(2,)
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(2,)
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[ 0.56410679  0.4999469 ]
(2,)
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(2,)
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[ 0.38288455  0.72674372]
(2,)
[ 0.68456994]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.42562701  0.60573668]
(2,)
[ 0.04934185]
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(2,)
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(2,)
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(2,)
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[ 0.49838861  0.50910703]
(2,)
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(2,)
[ 0.48411566  0.58579616]
(2,)
[-0.45340436]
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(2,)
[ 0.51838011  0.51404542]
(2,)
Progress: 12.0% ... Training loss: 0.884 ... Validation loss: 1.381[-0.56694965]
[ 0.50701367  0.3830862 ]
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[ 0.50701367  0.3830862 ]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.40510379  0.62491425]
(2,)
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(2,)
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(2,)
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[ 0.49183071  0.5451492 ]
(2,)
[ 2.32875998]
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(2,)
[ 0.57404463  0.48162164]
(2,)
[-0.63328403]
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(2,)
[ 0.6051025   0.39830885]
(2,)
Progress: 13.0% ... Training loss: 0.882 ... Validation loss: 1.379[ 0.97754516]
[ 0.44153522  0.62482083]
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(2,)
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(2,)
[ 0.46578995]
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[ 0.43347265  0.54402322]
(2,)
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[ 0.55213314  0.52619803]
(2,)
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(2,)
[ 0.50365456  0.56034048]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.56201538  0.37266395]
(2,)
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(2,)
[ 0.46159326  0.46690321]
(2,)
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(2,)
[ 0.4937539   0.44663087]
(2,)
Progress: 14.0% ... Training loss: 0.878 ... Validation loss: 1.374[-0.33971444]
[ 0.37968043  0.58389328]
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.37453922  0.68038306]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 4.31416288]
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[ 0.37837108  0.5554544 ]
(2,)
[ 1.63068056]
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(2,)
[ 0.4647243   0.48458429]
(2,)
[-0.86895382]
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(2,)
[ 0.58739825  0.65437337]
(2,)
Progress: 15.0% ... Training loss: 0.876 ... Validation loss: 1.370[-0.76562237]
[ 0.4929048  0.2996272]
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[ 0.51194402  0.57330253]
(2,)
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(2,)
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(2,)
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(2,)
[ 0.69712364]
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(2,)
[ 0.42506062  0.63453944]
(2,)
[ 0.79044418]
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(2,)
[ 0.33689854  0.44856277]
(2,)
[-0.6403453]
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(2,)
[ 0.57029055  0.37293473]
(2,)
Progress: 16.0% ... Training loss: 0.872 ... Validation loss: 1.366[ 0.04586473]
[ 0.70006425  0.41499475]
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(2,)
[ 0.70006425  0.41499475]
(2,)
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(2,)
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[ 0.37011754  0.57121353]
(2,)
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(2,)
[ 0.39624783  0.57166103]
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(2,)
[ 2.48978192]
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(2,)
[ 0.92879762]
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(2,)
[ 0.61393555  0.65709842]
(2,)
[ 0.56646206]
[ 0.27543193  0.7015438 ]
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(2,)
[ 0.27543193  0.7015438 ]
(2,)
Progress: 17.0% ... Training loss: 0.868 ... Validation loss: 1.359[ 0.0689868]
[ 0.55733646  0.53057911]
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.6124079   0.35071396]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.40563298  0.72451371]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.47545916  0.58553047]
(2,)
Progress: 18.0% ... Training loss: 0.862 ... Validation loss: 1.347[ 0.99883181]
[ 0.32941812  0.51293221]
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.52642987  0.53824439]
(2,)
[ 2.14029681]
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[ 0.45474299  0.6407937 ]
(2,)
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(2,)
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[ 0.50492319  0.51306349]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.39645589  0.54606805]
(2,)
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[ 0.44994529  0.65572503]
(2,)
Progress: 19.0% ... Training loss: 0.860 ... Validation loss: 1.348[ 0.18792995]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 1.98484808]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.69587677]
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(2,)
[ 0.46280869  0.52717987]
(2,)
[-0.29618466]
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(2,)
[ 0.59297208  0.46904057]
(2,)
Progress: 20.0% ... Training loss: 0.859 ... Validation loss: 1.353[ 0.45042763]
[ 0.43393316  0.48957061]
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.52499204  0.46112345]
(2,)
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[ 0.39671565  0.6847416 ]
(2,)
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(2,)
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[ 0.36065735  0.49865245]
(2,)
[ 0.14715343]
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(2,)
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(2,)
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[ 0.70597617  0.59591754]
(2,)
[ 1.20799522]
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(2,)
[-0.07866693]
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[ 0.54890418  0.46974899]
(2,)
[-0.26108934]
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(2,)
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[ 0.34161763  0.62541325]
(2,)
[ 1.59840117]
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[ 0.38100706  0.69517012]
(2,)
[ 2.47052018]
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(2,)
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[ 0.6497295   0.48362118]
(2,)
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[ 0.52527419  0.64237584]
(2,)
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(2,)
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(2,)
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[ 0.4014152   0.70352005]
(2,)
[ 0.87199284]
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[ 0.40157695  0.5898754 ]
(2,)
[ 3.35488739]
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(2,)
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(2,)
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[ 0.42043338  0.6839024 ]
(2,)
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[ 0.41158251  0.69056746]
(2,)
[-0.29904563]
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(2,)
[ 0.37954567  0.54785847]
(2,)
[-0.80592045]
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(2,)
[ 0.60013612  0.47955438]
(2,)
Progress: 21.0% ... Training loss: 0.857 ... Validation loss: 1.355[-0.70576111]
[ 0.58736859  0.43561125]
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(2,)
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(2,)
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(2,)
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[ 0.48999067  0.50621993]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.5423222   0.44185364]
(2,)
[ 1.83096157]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.63777929  0.31336985]
(2,)
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(2,)
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[ 0.39594768  0.5217476 ]
(2,)
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(2,)
[ 1.52357774]
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(2,)
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(2,)
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(2,)
[ 0.54806965  0.61142143]
(2,)
[-0.81597898]
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(2,)
[ 0.55888908  0.56124099]
(2,)
Progress: 22.0% ... Training loss: 0.853 ... Validation loss: 1.348[-0.93305942]
[ 0.44588446  0.43866391]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.33406692]
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[ 0.48984133  0.5543036 ]
(2,)
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(2,)
[ 0.45780519  0.51824642]
(2,)
[-0.30259936]
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(2,)
[ 0.43047319  0.31850887]
(2,)
[-0.57609814]
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(2,)
[ 0.56625734  0.33209941]
(2,)
Progress: 23.0% ... Training loss: 0.851 ... Validation loss: 1.350[-0.00378008]
[ 0.51320386  0.44812953]
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(2,)
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(2,)
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[ 0.46943055  0.52193694]
(2,)
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[ 0.44015333  0.46574828]
(2,)
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(2,)
[ 0.44083638  0.73377295]
(2,)
[-1.00726935]
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(2,)
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(2,)
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(2,)
[ 0.55932044  0.45396968]
(2,)
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[ 0.37430555  0.49627142]
(2,)
[-0.361202]
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.38946253  0.5519417 ]
(2,)
[ 1.40288591]
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(2,)
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[ 0.43467762  0.67003979]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.45680851  0.59801129]
(2,)
Progress: 24.0% ... Training loss: 0.845 ... Validation loss: 1.335[-0.49184837]
[ 0.59383982  0.39480712]
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(2,)
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(2,)
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(2,)
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[ 0.34746386  0.47070931]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.37501501  0.63978931]
(2,)
[ 1.06045916]
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(2,)
[ 0.41527989  0.54803384]
(2,)
[-0.48177285]
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(2,)
[ 0.54639022  0.39064883]
(2,)
Progress: 25.0% ... Training loss: 0.842 ... Validation loss: 1.329[-0.16228386]
[ 0.35697261  0.67655491]
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(2,)
[ 2.79673683]
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.68667696  0.53177929]
(2,)
[ 0.77309981]
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(2,)
[ 0.487256    0.46996206]
(2,)
[ 2.44301089]
[ 0.3042262   0.69115362]
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(2,)
[ 0.3042262   0.69115362]
(2,)
Progress: 26.0% ... Training loss: 0.838 ... Validation loss: 1.323[-0.70354043]
[ 0.71965343  0.43327224]
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(2,)
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(2,)
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[ 0.3575463   0.71934085]
(2,)
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(2,)
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(2,)
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(2,)
[ 0.46156639  0.63957446]
(2,)
[-0.0441743]
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(2,)
[ 0.46326535  0.569617  ]
(2,)
Progress: 27.0% ... Training loss: 0.834 ... Validation loss: 1.316[ 0.37863001]
[ 0.48071881  0.35669003]
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.65490837  0.32193971]
(2,)
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(2,)
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(2,)
[ 2.4871544]
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.51206466  0.52076481]
(2,)
Progress: 28.0% ... Training loss: 0.831 ... Validation loss: 1.311[-0.79505733]
[ 0.62970499  0.49003363]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.3394251   0.63245723]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.64179178  0.54706363]
(2,)
Progress: 29.0% ... Training loss: 0.829 ... Validation loss: 1.312[-0.04266702]
[ 0.46557139  0.50058856]
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.47430062  0.61117238]
(2,)
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(2,)
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(2,)
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[ 0.58363619  0.52661108]
(2,)
[ 1.13152486]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.53014481]
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(2,)
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(2,)
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(2,)
Progress: 30.0% ... Training loss: 0.826 ... Validation loss: 1.306[ 0.53254732]
[ 0.38805641  0.56294111]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.61837629  0.39316632]
(2,)
[ 1.06547126]
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.35155263  0.7564301 ]
(2,)
[-1.09625658]
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(2,)
[ 0.42735513  0.62917283]
(2,)
[-0.52298728]
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(2,)
[ 0.34351799  0.54931297]
(2,)
Progress: 31.0% ... Training loss: 0.823 ... Validation loss: 1.303[-0.95305551]
[ 0.56213224  0.58321255]
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(2,)
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(2,)
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[ 0.57391636  0.29502155]
(2,)
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[ 0.60470732  0.30251416]
(2,)
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(2,)
[ 0.22249248  0.63976271]
(2,)
[-0.62714749]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[-0.8627683]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 3.11746971]
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(2,)
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(2,)
[ 0.28844663  0.68615387]
(2,)
[ 0.20188226]
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(2,)
[ 0.3469554   0.67355554]
(2,)
Progress: 32.0% ... Training loss: 0.821 ... Validation loss: 1.298[-0.60148093]
[ 0.58034536  0.35240368]
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(2,)
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(2,)
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[ 0.49451365  0.57136897]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.41313628  0.55792205]
(2,)
[-0.63961324]
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(2,)
[ 0.46821133  0.62056436]
(2,)
Progress: 33.0% ... Training loss: 0.818 ... Validation loss: 1.294[-0.37411923]
[ 0.23080656  0.70943095]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.28029399  0.72852645]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
Progress: 34.0% ... Training loss: 0.816 ... Validation loss: 1.296[-0.19956167]
[ 0.44904876  0.58748059]
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(2,)
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(2,)
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(2,)
[ 1.05030821]
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(2,)
[ 0.45434745  0.68387104]
(2,)
[-1.01177617]
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(2,)
[ 0.45480043  0.53829298]
(2,)
Progress: 35.0% ... Training loss: 0.813 ... Validation loss: 1.290[-0.75619117]
[ 0.38082801  0.54353827]
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(2,)
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(2,)
[ 2.25951208]
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(2,)
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(2,)
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(2,)
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[ 0.4950598   0.55175962]
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[ 0.45960479  0.53185936]
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[ 0.52355371  0.5787708 ]
(2,)
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[ 0.39860948  0.66909443]
(2,)
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(2,)
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(2,)
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[ 0.47225432  0.4417091 ]
(2,)
[-0.33753072]
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(2,)
[ 0.35775533  0.58135668]
(2,)
[ 1.34275115]
[ 0.26909108  0.65716896]
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(2,)
[ 0.26909108  0.65716896]
(2,)
Progress: 36.0% ... Training loss: 0.810 ... Validation loss: 1.284[ 0.24945329]
[ 0.50049293  0.55518995]
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(2,)
[ 0.50049293  0.55518995]
(2,)
[ 0.56046083]
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(2,)
[ 0.3246017   0.68644928]
(2,)
[-0.58592012]
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(2,)
[ 0.45361854  0.51745297]
(2,)
[ 2.02867761]
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(2,)
[ 0.277659    0.68555948]
(2,)
[-0.66899706]
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(2,)
[ 0.48475251  0.49058433]
(2,)
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(2,)
[ 0.31003121  0.59421095]
(2,)
[ 0.15901293]
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(2,)
[ 0.78260414  0.33556626]
(2,)
[-0.25619756]
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(2,)
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(2,)
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(2,)
[ 0.34039583  0.65223898]
(2,)
[ 1.67341625]
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(2,)
[ 0.45585173  0.59303737]
(2,)
Progress: 37.0% ... Training loss: 0.807 ... Validation loss: 1.284[-0.77663024]
[ 0.66934842  0.46957505]
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(2,)
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[ 0.62737226  0.55032517]
(2,)
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[ 0.38120126  0.61480319]
(2,)
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(2,)
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(2,)
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[ 0.56130614  0.52891949]
(2,)
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(2,)
[ 1.20028218]
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[ 0.53039006  0.38455409]
(2,)
[ 0.20639452]
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[ 0.29071108  0.69344724]
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(2,)
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[ 0.47194314  0.5564638 ]
(2,)
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[ 0.3400709   0.58830109]
(2,)
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[ 0.30118716  0.6993095 ]
(2,)
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[ 0.58441651  0.30102486]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.72141652  0.53448535]
(2,)
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(2,)
[ 0.41736049  0.69030092]
(2,)
Progress: 38.0% ... Training loss: 0.804 ... Validation loss: 1.283[-0.33301951]
[ 0.64208002  0.46585096]
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.32410626  0.62930162]
(2,)
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(2,)
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(2,)
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(2,)
[ 0.54785614  0.39162533]
(2,)
[-0.72992396]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.46222988  0.65920892]
(2,)
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(2,)
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(2,)
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(2,)
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[ 0.40443299  0.6538554 ]
(2,)
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(2,)
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(2,)
[ 0.76223059  0.20270662]
(2,)
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(2,)
[ 0.65098159  0.36095685]
(2,)
Progress: 39.0% ... Training loss: 0.802 ... Validation loss: 1.284[-0.53967407]
[ 0.43595271  0.4465061 ]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.48329397  0.39494793]
(2,)
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(2,)
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[ 0.56580706  0.5281571 ]
(2,)
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[ 0.26264521  0.64553537]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.29121196  0.53857022]
(2,)
[-0.39307325]
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(2,)
[ 0.5093477   0.43126973]
(2,)
[ 1.11777756]
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(2,)
[ 0.44458624  0.58111567]
(2,)
Progress: 40.0% ... Training loss: 0.800 ... Validation loss: 1.289[ 0.17992629]
[ 0.48203545  0.48686048]
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.47824584  0.62901356]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[-0.40578748]
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.39730264  0.5861093 ]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.31217519  0.65519221]
(2,)
[ 1.47066283]
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(2,)
[ 0.2645514   0.72163292]
(2,)
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(2,)
[ 0.52905703  0.44983603]
(2,)
[-0.61046785]
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[ 0.40980556  0.46510668]
(2,)
[-1.09370529]
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(2,)
[ 0.38812472  0.60875881]
(2,)
[ 0.85546862]
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(2,)
[ 0.44998822  0.47222724]
(2,)
[-1.04102083]
[ 0.30588899  0.51033476]
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(2,)
[ 0.30588899  0.51033476]
(2,)
Progress: 41.0% ... Training loss: 0.797 ... Validation loss: 1.287[ 0.62718553]
[ 0.35266213  0.51930993]
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(2,)
[ 0.35266213  0.51930993]
(2,)
[-0.65090424]
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(2,)
[ 0.47508304  0.59746148]
(2,)
[ 2.09194912]
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(2,)
[ 0.64178215  0.39108531]
(2,)
[-0.23453319]
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(2,)
[ 0.24060703  0.69127336]
(2,)
[ 2.440718]
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(2,)
[ 0.38715631  0.79139963]
(2,)
[-1.00924494]
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(2,)
[ 0.4640239  0.6196233]
(2,)
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(2,)
[ 0.48150571  0.43143084]
(2,)
[-0.52757245]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[-0.16847295]
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(2,)
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[ 0.37159898  0.5959005 ]
(2,)
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(2,)
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(2,)
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[ 0.29556689  0.61056615]
(2,)
[-0.43100514]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.61221438  0.22621324]
(2,)
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(2,)
[ 0.61810179  0.46247977]
(2,)
[ 2.12763455]
[ 0.31013668  0.63759292]
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(2,)
[ 0.31013668  0.63759292]
(2,)
Progress: 42.0% ... Training loss: 0.796 ... Validation loss: 1.291[-1.10504175]
[ 0.40066227  0.68327313]
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(2,)
[ 0.40066227  0.68327313]
(2,)
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(2,)
[ 0.27608216  0.57302391]
(2,)
[ 3.2650632]
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(2,)
[ 0.36708047  0.62896983]
(2,)
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(2,)
[ 0.44319672  0.50035655]
(2,)
[-0.2615961]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.24045984  0.74098376]
(2,)
[-0.35466674]
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(2,)
[ 0.4450392   0.53598759]
(2,)
[ 1.227984]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.48720182  0.50784847]
(2,)
[ 1.22595822]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.33096928  0.46723275]
(2,)
[ 0.7784784]
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(2,)
[ 0.34704957  0.59686372]
(2,)
[-0.73020514]
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(2,)
[ 0.54086479  0.57045923]
(2,)
Progress: 43.0% ... Training loss: 0.794 ... Validation loss: 1.283[-0.88673517]
[ 0.53688232  0.51424527]
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(2,)
[ 0.382844]
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(2,)
[ 0.55819175  0.46013231]
(2,)
[ 0.60116312]
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(2,)
[ 0.71472332  0.37804861]
(2,)
Progress: 44.0% ... Training loss: 0.792 ... Validation loss: 1.281[-0.76781509]
[ 0.5872619   0.38074211]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
Progress: 45.0% ... Training loss: 0.790 ... Validation loss: 1.280[-0.45615325]
[ 0.39500218  0.63085758]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.54935065  0.52108373]
(2,)
[-0.30830219]
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(2,)
[ 0.71465047  0.42485357]
(2,)
Progress: 46.0% ... Training loss: 0.788 ... Validation loss: 1.285[ 0.2137659]
[ 0.4265108   0.37630734]
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.58930054  0.58432513]
(2,)
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[ 0.43110857  0.54761146]
(2,)
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[ 0.34235024  0.65744071]
(2,)
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[ 0.37431034  0.49738287]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.53291617  0.44797555]
(2,)
Progress: 47.0% ... Training loss: 0.786 ... Validation loss: 1.286[-0.63840086]
[ 0.67345176  0.5406206 ]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
Progress: 48.0% ... Training loss: 0.783 ... Validation loss: 1.281[-0.78093414]
[ 0.62511178  0.4678458 ]
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.59772647  0.35577704]
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[ 0.40143476  0.58490083]
(2,)
[ 0.28330833]
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(2,)
[ 3.12675196]
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[ 0.30428006  0.85043428]
(2,)
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[ 0.35423876  0.52890865]
(2,)
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[ 0.489246    0.59177105]
(2,)
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[ 0.36484016  0.57644212]
(2,)
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[ 0.50253426  0.61511114]
(2,)
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[ 0.36439263  0.47641476]
(2,)
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(2,)
[ 0.53961394  0.33624851]
(2,)
[ 0.30136466]
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(2,)
[ 0.41778321  0.64326514]
(2,)
[-0.17434264]
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(2,)
[ 0.47145927  0.56436289]
(2,)
Progress: 49.0% ... Training loss: 0.780 ... Validation loss: 1.280[ 0.55626134]
[ 0.34743251  0.65252719]
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(2,)
[ 0.34743251  0.65252719]
(2,)
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(2,)
[ 0.3802549   0.55551106]
(2,)
[-0.36752728]
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(2,)
[ 0.33185225  0.69318581]
(2,)
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(2,)
[ 0.34724086  0.63346116]
(2,)
[ 0.88289463]
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(2,)
[ 0.27298627  0.68380307]
(2,)
[ 1.50481339]
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(2,)
[ 0.5421298   0.53817564]
(2,)
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(2,)
[ 0.35011818  0.59179399]
(2,)
[ 1.74834402]
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.25962871  0.62839339]
(2,)
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(2,)
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(2,)
[ 1.05656872]
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[ 0.40029825  0.57302477]
(2,)
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(2,)
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(2,)
[ 0.35343505  0.68658577]
(2,)
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(2,)
[ 0.44638035  0.64717645]
(2,)
[-1.13520692]
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[ 0.31396009  0.66538901]
(2,)
[-0.81596734]
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[ 0.12703139  0.73349371]
(2,)
[ 0.0946241]
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(2,)
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[ 0.32130864  0.6292283 ]
(2,)
[ 2.1839281]
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[ 0.30282399  0.80582103]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.30010337  0.70598836]
(2,)
Progress: 50.0% ... Training loss: 0.777 ... Validation loss: 1.277[ 0.03565605]
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(2,)
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(2,)
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[ 0.63806916  0.38504009]
(2,)
[ 0.8445961]
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(2,)
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(2,)
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(2,)
[ 0.54483921  0.44643025]
(2,)
[-1.1483322]
[ 0.3484711   0.62400386]
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(2,)
[ 0.3484711   0.62400386]
(2,)
Progress: 51.0% ... Training loss: 0.775 ... Validation loss: 1.277[-0.45210528]
[ 0.62827589  0.481842  ]
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(2,)
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(2,)
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[ 0.56090681  0.39889217]
(2,)
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(2,)
[ 0.36362808  0.71873351]
(2,)
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(2,)
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(2,)
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(2,)
[-0.55114168]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.16830018  0.62830972]
(2,)
Progress: 52.0% ... Training loss: 0.774 ... Validation loss: 1.281[-0.46510032]
[ 0.48247423  0.48380016]
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(2,)
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(2,)
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[ 0.38019808  0.62911757]
(2,)
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[ 0.396423    0.63551377]
(2,)
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(2,)
[ 0.41904086  0.63936411]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.60746282  0.5258613 ]
(2,)
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[ 0.3777196   0.40674701]
(2,)
[ 0.64131519]
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(2,)
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(2,)
[ 0.44328134]
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(2,)
[ 0.44346574  0.61807919]
(2,)
[ 0.79698412]
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(2,)
[ 0.36684254  0.61716909]
(2,)
[ 1.80109654]
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(2,)
[ 0.22628081  0.8154869 ]
(2,)
[-0.06112785]
[ 0.71899317  0.4195583 ]
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(2,)
[ 0.71899317  0.4195583 ]
(2,)
Progress: 53.0% ... Training loss: 0.772 ... Validation loss: 1.276[ 0.03635444]
[ 0.52705456  0.56379003]
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(2,)
[ 0.52705456  0.56379003]
(2,)
[-0.15894743]
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(2,)
[ 0.44063523  0.56141677]
(2,)
[ 1.65437101]
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(2,)
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(2,)
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(2,)
[ 0.67378801  0.42257727]
(2,)
[-0.14852457]
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(2,)
[ 0.50496443  0.50473199]
(2,)
Progress: 54.0% ... Training loss: 0.770 ... Validation loss: 1.273[-0.61392523]
[ 0.67999127  0.44521447]
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[ 0.39003117  0.65988126]
(2,)
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(2,)
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(2,)
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(2,)
[ 2.08628653]
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(2,)
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(2,)
[-0.56722322]
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(2,)
[ 0.24826572  0.73912165]
(2,)
Progress: 55.0% ... Training loss: 0.768 ... Validation loss: 1.274[-0.52146052]
[ 0.4383656   0.49044002]
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(2,)
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(2,)
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(2,)
[ 0.61386068  0.43253883]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.26191553]
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(2,)
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(2,)
Progress: 56.0% ... Training loss: 0.765 ... Validation loss: 1.269[-0.06724758]
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(2,)
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(2,)
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(2,)
[ 0.30371121  0.5816486 ]
(2,)
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(2,)
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(2,)
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(2,)
[-0.33431275]
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(2,)
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[ 0.72024903  0.21866894]
(2,)
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[ 0.70004709  0.2319979 ]
(2,)
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[ 0.19152576  0.83572553]
(2,)
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(2,)
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(2,)
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(2,)
[-0.27060491]
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(2,)
[ 0.49346535  0.57466283]
(2,)
[ 0.76512818]
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(2,)
[ 0.23261143  0.66988699]
(2,)
Progress: 57.0% ... Training loss: 0.762 ... Validation loss: 1.262[-0.7291973]
[ 0.48656162  0.44521425]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.2879307   0.64633551]
(2,)
[-0.6243616]
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(2,)
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(2,)
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[ 0.36971086  0.59905867]
(2,)
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[ 0.23176401  0.67995207]
(2,)
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[ 0.35185292  0.53759698]
(2,)
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[ 0.41047273  0.47157403]
(2,)
[ 0.24769755]
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[ 0.32301603  0.67881541]
(2,)
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[ 0.32462802  0.51659725]
(2,)
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[ 0.52023706  0.37180765]
(2,)
[ 2.04175216]
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(2,)
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[ 0.73159994  0.42535742]
(2,)
[-0.1213794]
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(2,)
[ 0.19148751  0.70322282]
(2,)
[-0.17834522]
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(2,)
[ 0.54880188  0.51012754]
(2,)
Progress: 58.0% ... Training loss: 0.761 ... Validation loss: 1.263[ 0.17869772]
[ 0.16468266  0.75044934]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.52741093  0.44640253]
(2,)
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(2,)
[ 0.49166178  0.2826969 ]
(2,)
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(2,)
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(2,)
[ 3.02757139]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.31492126  0.63929643]
(2,)
[ 1.21550323]
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(2,)
[ 0.15867081  0.79662561]
(2,)
[ 0.64758935]
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(2,)
[ 0.27670122  0.74236745]
(2,)
[ 2.01246365]
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(2,)
[ 0.18980694  0.72373206]
(2,)
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(2,)
[ 0.48124866  0.47362475]
(2,)
[ 0.84501827]
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(2,)
[ 0.24996253  0.62863623]
(2,)
[-0.33893564]
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(2,)
[ 0.50250549  0.54584997]
(2,)
[-0.69786456]
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(2,)
[ 0.26206167  0.69713728]
(2,)
[-0.41413021]
[ 0.35256683  0.63879001]
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(2,)
[ 0.35256683  0.63879001]
(2,)
Progress: 59.0% ... Training loss: 0.759 ... Validation loss: 1.261[-0.30346091]
[ 0.23487503  0.63142141]
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(2,)
[ 0.23487503  0.63142141]
(2,)
[-1.13849967]
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[ 0.3776551   0.65951558]
(2,)
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(2,)
[ 0.65093873  0.38551289]
(2,)
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(2,)
[ 0.28308073  0.57456126]
(2,)
[-0.46825347]
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(2,)
[ 0.61335709  0.4129563 ]
(2,)
[ 2.76994847]
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(2,)
[ 0.28543782  0.76632493]
(2,)
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(2,)
[ 0.39722016  0.7290228 ]
(2,)
[-0.32624061]
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(2,)
[ 0.3452474   0.62984901]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.54263055  0.3652154 ]
(2,)
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[ 0.46504202  0.57856548]
(2,)
[-0.6096073]
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(2,)
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(2,)
[ 0.6360903   0.46416538]
(2,)
[ 1.38038571]
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[ 0.18701623  0.81122698]
(2,)
[ 1.58671781]
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[ 0.29946413  0.6126503 ]
(2,)
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(2,)
[ 0.69187709  0.37738665]
(2,)
[-1.09716979]
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[ 0.43343725  0.65230473]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.28765571  0.6932849 ]
(2,)
[ 0.17652464]
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(2,)
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[ 0.40436771  0.43821245]
(2,)
[ 0.59618101]
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(2,)
[ 0.44737848  0.39197551]
(2,)
[-0.65527361]
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(2,)
[ 0.17722042  0.74647241]
(2,)
Progress: 60.0% ... Training loss: 0.757 ... Validation loss: 1.261[-0.71721957]
[ 0.60250348  0.54459515]
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(2,)
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[ 0.57678005  0.41021228]
(2,)
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(2,)
[ 0.3938396   0.43530074]
(2,)
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(2,)
[ 0.17155102  0.7123458 ]
(2,)
[-0.39998841]
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(2,)
[ 0.32420064  0.6873621 ]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.4823715  0.5488008]
(2,)
[-0.30821809]
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(2,)
Progress: 61.0% ... Training loss: 0.755 ... Validation loss: 1.263[ 0.22700567]
[ 0.55299244  0.51241968]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 1.19890381]
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(2,)
[ 0.23595074  0.73364754]
(2,)
[ 0.69692801]
[ 0.20805084  0.66463963]
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(2,)
[ 0.20805084  0.66463963]
(2,)
Progress: 62.0% ... Training loss: 0.754 ... Validation loss: 1.263[-0.08812844]
[ 0.22507844  0.73640038]
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(2,)
[ 0.22507844  0.73640038]
(2,)
[ 0.37320105]
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(2,)
[ 0.2264648   0.66616532]
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(2,)
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(2,)
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(2,)
[ 0.71807223  0.32260932]
(2,)
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[ 0.30344335  0.6163213 ]
(2,)
Progress: 63.0% ... Training loss: 0.752 ... Validation loss: 1.258[-0.37203171]
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(2,)
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[ 0.43377301  0.71770054]
(2,)
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(2,)
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[ 0.67675025  0.38847349]
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(2,)
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(2,)
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[ 0.22876452  0.76500748]
(2,)
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[ 0.2306442   0.71238502]
(2,)
[-0.04358934]
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(2,)
[ 0.35966402  0.6125636 ]
(2,)
[-0.55463303]
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(2,)
[ 0.72020732  0.34577631]
(2,)
Progress: 64.0% ... Training loss: 0.749 ... Validation loss: 1.247[-0.06872598]
[ 0.58673188  0.55793061]
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.52202133  0.64571659]
(2,)
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[ 0.34484535  0.69551112]
(2,)
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(2,)
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[ 0.28336632  0.53001143]
(2,)
[-0.61011097]
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(2,)
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[ 0.62160462  0.42536758]
(2,)
[ 0.32377603]
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[ 0.38223976  0.61726916]
(2,)
[ 0.34595485]
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[ 0.22077946  0.67218957]
(2,)
[ 1.14509493]
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[ 0.32832654  0.57670674]
(2,)
[-0.36055183]
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[ 0.46175251  0.4547809 ]
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[ 0.39814344]
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[ 0.33299474  0.61189738]
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[ 0.55918417  0.44491305]
(2,)
[ 1.53725571]
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[ 0.22350186  0.68809816]
(2,)
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(2,)
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[ 0.3007645   0.67716895]
(2,)
[ 1.00428749]
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[ 0.493923    0.53686194]
(2,)
[ 2.31612321]
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(2,)
[-0.8522697]
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(2,)
[ 0.51894575  0.51392002]
(2,)
[ 0.33686067]
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(2,)
[ 0.17094824  0.73905499]
(2,)
Progress: 65.0% ... Training loss: 0.747 ... Validation loss: 1.245[ 0.03990596]
[ 0.38047906  0.65634235]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.4129862   0.41732938]
(2,)
[ 2.17825573]
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(2,)
[ 0.47272098  0.49095441]
(2,)
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(2,)
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(2,)
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[ 0.50620675  0.49572108]
(2,)
[-0.44041114]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.39293493  0.54208545]
(2,)
Progress: 66.0% ... Training loss: 0.746 ... Validation loss: 1.248[-0.21989836]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.29393436  0.57975501]
(2,)
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(2,)
[ 0.45531398]
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(2,)
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[ 0.54094092  0.62438001]
(2,)
[ 1.23676419]
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(2,)
[ 0.32335586  0.68246889]
(2,)
[ 0.98716095]
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(2,)
[ 0.30131021  0.66316442]
(2,)
Progress: 67.0% ... Training loss: 0.744 ... Validation loss: 1.247[ 2.08416547]
[ 0.26448573  0.72000824]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.42609499]
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(2,)
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(2,)
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(2,)
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[ 0.4491607   0.60303092]
(2,)
[ 1.5512729]
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(2,)
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(2,)
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(2,)
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(2,)
[-0.51476404]
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(2,)
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(2,)
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(2,)
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[ 0.31077479  0.69546298]
(2,)
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(2,)
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(2,)
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[ 0.34971694  0.72301595]
(2,)
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(2,)
[ 0.44841709  0.40902736]
(2,)
Progress: 68.0% ... Training loss: 0.742 ... Validation loss: 1.242[ 0.27198556]
[ 0.44347652  0.44953579]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.3574625   0.54461761]
(2,)
[ 1.53332972]
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(2,)
[ 0.25363152  0.718718  ]
(2,)
[-1.20407952]
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(2,)
[ 0.3710738   0.67439787]
(2,)
[ 1.32511225]
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(2,)
[ 0.16398521  0.72847768]
(2,)
[-0.51025444]
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(2,)
[ 0.46804955  0.40830761]
(2,)
[ 0.29265808]
[ 0.21296976  0.72656686]
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(2,)
[ 0.21296976  0.72656686]
(2,)
Progress: 69.0% ... Training loss: 0.740 ... Validation loss: 1.238[-0.38741062]
[ 0.6398776  0.3155917]
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(2,)
[ 0.6398776  0.3155917]
(2,)
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[ 0.59135825  0.57296131]
(2,)
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(2,)
[ 0.62722943  0.453136  ]
(2,)
[-0.6279494]
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(2,)
[ 0.6025588   0.40181963]
(2,)
[-0.56555581]
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(2,)
[ 0.55249811  0.34425892]
(2,)
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(2,)
[ 0.62467169  0.29384202]
(2,)
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(2,)
[ 0.56984646  0.3047666 ]
(2,)
[-0.15599599]
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(2,)
[ 0.46684329  0.42895399]
(2,)
[ 2.38110972]
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(2,)
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(2,)
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(2,)
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[ 0.51382498  0.37775457]
(2,)
[ 1.31061753]
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(2,)
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[ 0.19692578  0.84648822]
(2,)
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[ 0.28933045  0.63661408]
(2,)
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(2,)
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(2,)
[ 0.49573564  0.52680323]
(2,)
[-0.09809968]
[ 0.35816082  0.56080519]
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(2,)
[ 0.35816082  0.56080519]
(2,)
Progress: 70.0% ... Training loss: 0.738 ... Validation loss: 1.238[-0.60636883]
[ 0.61458517  0.26069075]
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(2,)
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(2,)
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(2,)
[ 0.674907    0.45205362]
(2,)
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(2,)
[ 0.61516432  0.37550566]
(2,)
[-0.50582801]
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(2,)
[ 0.2282263   0.74422359]
(2,)
[-0.22429126]
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(2,)
[ 0.20241092  0.73095259]
(2,)
[-0.7917121]
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(2,)
[ 0.25588178  0.6754474 ]
(2,)
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(2,)
[ 0.50423232  0.37563501]
(2,)
[-0.37005833]
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(2,)
[ 0.77275392  0.24692624]
(2,)
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(2,)
[ 0.31428516  0.55100884]
(2,)
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(2,)
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(2,)
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(2,)
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[ 0.50096801  0.50655219]
(2,)
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(2,)
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(2,)
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[ 0.75046088  0.3487437 ]
(2,)
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[ 0.2504371   0.74748261]
(2,)
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(2,)
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(2,)
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(2,)
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[ 0.57673463  0.4083865 ]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 1.79714883]
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(2,)
[ 0.25751632  0.68649025]
(2,)
[ 2.73159592]
[ 0.21657774  0.86875386]
[ 1.          0.          0.74270727 -2.05892905  0.51988127  0.          1.
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(2,)
[ 0.21657774  0.86875386]
(2,)
Progress: 71.0% ... Training loss: 0.736 ... Validation loss: 1.243[-0.60471954]
[ 0.476307   0.5111956]
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(2,)
[ 0.476307   0.5111956]
(2,)
[ 2.37483116]
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(2,)
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(2,)
[ 0.49672716]
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(2,)
Progress: 72.0% ... Training loss: 0.734 ... Validation loss: 1.244[-0.61241032]
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[ 0.34773822  0.62224106]
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(2,)
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[ 0.51104922  0.55785636]
(2,)
[-0.04618249]
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(2,)
[ 0.30526964  0.68106991]
(2,)
[ 1.10251239]
[ 0.33052353  0.70432105]
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(2,)
[ 0.33052353  0.70432105]
(2,)
Progress: 73.0% ... Training loss: 0.733 ... Validation loss: 1.247[-0.50726053]
[ 0.71264599  0.30871983]
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(2,)
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(2,)
[ 1.00221015]
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[ 0.54913946  0.55773142]
(2,)
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(2,)
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(2,)
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(2,)
[ 0.39683595  0.48214446]
(2,)
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[ 0.26888988  0.572855  ]
(2,)
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(2,)
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(2,)
[ 2.69066164]
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[ 0.37142062  0.68277374]
(2,)
[ 1.02141238]
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(2,)
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(2,)
[ 0.27402925  0.74013393]
(2,)
[ 0.15194894]
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(2,)
[ 0.33058867  0.53740712]
(2,)
Progress: 74.0% ... Training loss: 0.732 ... Validation loss: 1.243[-1.1452324]
[ 0.38523649  0.70708067]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.40299917  0.59662651]
(2,)
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[ 0.35214783  0.54893355]
(2,)
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[ 0.72574219  0.42357149]
(2,)
[ 1.54959558]
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[ 0.50241418  0.58868139]
(2,)
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[ 0.73509475  0.30825751]
(2,)
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[ 0.58824488  0.324518  ]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.7727448   0.26523582]
(2,)
[ 0.50070397]
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(2,)
[ 0.61999757  0.30521013]
(2,)
Progress: 75.0% ... Training loss: 0.730 ... Validation loss: 1.234[ 1.99230636]
[ 0.20926077  0.77927384]
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(2,)
[ 0.20926077  0.77927384]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.43553447  0.48569491]
(2,)
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(2,)
[ 0.27400388  0.5037071 ]
(2,)
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(2,)
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(2,)
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(2,)
[ 0.60565714  0.35177006]
(2,)
[ 2.88088054]
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(2,)
[ 0.14554882  0.84980102]
(2,)
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(2,)
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(2,)
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[ 0.70761632  0.21938403]
(2,)
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(2,)
[-1.09389473]
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.32641806  0.69674496]
(2,)
[ 0.81200906]
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[ 0.31962542  0.65306817]
(2,)
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[ 0.60377499  0.39347879]
(2,)
[ 0.06186589]
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[ 0.62430167  0.30945803]
(2,)
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(2,)
[ 0.15182147  0.83410774]
(2,)
[ 1.56742601]
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[ 0.13890904  0.78632346]
(2,)
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(2,)
[ 0.3837039   0.65342387]
(2,)
[ 0.03029244]
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(2,)
[ 0.43073479  0.51331308]
(2,)
[-0.49217348]
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(2,)
[ 0.71012366  0.4072237 ]
(2,)
[-0.22985255]
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(2,)
[ 0.46135892  0.65989922]
(2,)
[ 1.19731365]
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(2,)
[ 0.42976725  0.53930979]
(2,)
[-0.19371766]
[ 0.34966143  0.58615032]
[ 0.          1.          0.01564651 -0.40029496  0.03189788  0.          1.
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(2,)
[ 0.34966143  0.58615032]
(2,)
Progress: 76.0% ... Training loss: 0.728 ... Validation loss: 1.231[-0.64043181]
[ 0.63046911  0.36996082]
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(2,)
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(2,)
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(2,)
[ 0.28027331  0.74800182]
(2,)
[ 0.39032112]
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(2,)
[ 0.61617356  0.38990468]
(2,)
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(2,)
[ 0.54495501  0.41839256]
(2,)
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(2,)
[ 0.71082785  0.392437  ]
(2,)
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(2,)
[ 0.64526663  0.44286988]
(2,)
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(2,)
[ 0.35105739  0.67292261]
(2,)
[ 2.24473018]
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(2,)
[ 0.15027582  0.70831958]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.64285976  0.39503698]
(2,)
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[ 0.35407662  0.52051607]
(2,)
[ 0.46268517]
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(2,)
[ 0.27247885  0.70556097]
(2,)
[-0.53525904]
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(2,)
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(2,)
[ 0.29932217  0.60154929]
(2,)
[ 0.9451292]
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[ 0.36983726  0.55464016]
(2,)
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[ 0.1812373   0.78617309]
(2,)
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(2,)
[ 0.40395025  0.54687368]
(2,)
[-0.93226525]
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(2,)
[ 0.52017173  0.55121411]
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(2,)
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(2,)
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[ 0.14093567  0.80075022]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.53520565  0.44261638]
(2,)
[-0.19903922]
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(2,)
[ 0.54519127  0.44479076]
(2,)
Progress: 77.0% ... Training loss: 0.726 ... Validation loss: 1.235[-1.12840075]
[ 0.42892719  0.68430447]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.52253422  0.55382147]
(2,)
[ 1.13963899]
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[ 0.40619063  0.65953551]
(2,)
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(2,)
[ 0.79741797  0.20773249]
(2,)
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(2,)
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(2,)
[ 0.3737908   0.61994217]
(2,)
[-0.05525192]
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(2,)
[ 0.29846902  0.69172531]
(2,)
[ 0.43377449]
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(2,)
[ 0.11849802  0.81207044]
(2,)
Progress: 78.0% ... Training loss: 0.725 ... Validation loss: 1.236[-0.52936245]
[ 0.70703402  0.4186211 ]
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(2,)
[ 0.70703402  0.4186211 ]
(2,)
[ 0.13470944]
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[ 0.7582568   0.26715013]
(2,)
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(2,)
[ 0.24424711  0.61621093]
(2,)
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(2,)
[ 0.18052261  0.77655243]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.42993882]
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(2,)
[ 0.11853856  0.83654452]
(2,)
[ 0.3079265]
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(2,)
[ 0.70416744  0.33426908]
(2,)
[-1.1320022]
[ 0.30791878  0.63212155]
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(2,)
[ 0.30791878  0.63212155]
(2,)
Progress: 79.0% ... Training loss: 0.723 ... Validation loss: 1.228[ 0.4427694]
[ 0.15676851  0.83931621]
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(2,)
[ 0.15676851  0.83931621]
(2,)
[-0.19707851]
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(2,)
[ 0.46697887  0.57676088]
(2,)
[ 0.46307781]
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(2,)
[ 0.29889197  0.74000767]
(2,)
[-0.35137609]
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(2,)
[ 0.40253436  0.58919453]
(2,)
[ 0.10547954]
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(2,)
[ 0.67350092  0.29678332]
(2,)
[ 1.48493436]
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(2,)
[ 0.48490024  0.58610803]
(2,)
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(2,)
[ 0.59414885  0.502441  ]
(2,)
[-0.52662246]
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(2,)
[ 0.47387304  0.31198767]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.48136873  0.5306321 ]
(2,)
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[ 0.71717407  0.4716079 ]
(2,)
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(2,)
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[ 0.60360584  0.53536993]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 3.3460361]
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(2,)
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(2,)
[-0.42087837]
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(2,)
[ 0.73421237  0.33819112]
(2,)
[-0.583079]
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(2,)
[ 0.40485336  0.62933991]
(2,)
Progress: 80.0% ... Training loss: 0.722 ... Validation loss: 1.227[-0.46077536]
[ 0.64492379  0.3471064 ]
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[ 0.13163441  0.89918849]
(2,)
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[ 0.15480122  0.75594112]
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(2,)
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[ 0.47517467  0.46375639]
(2,)
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(2,)
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(2,)
[ 0.661635    0.54352337]
(2,)
[-0.5196217]
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(2,)
[ 0.67908337  0.3937607 ]
(2,)
[-0.44570453]
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(2,)
[ 0.62435381  0.32931711]
(2,)
Progress: 81.0% ... Training loss: 0.720 ... Validation loss: 1.224[-0.2405324]
[ 0.38607957  0.48537911]
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(2,)
[ 0.38607957  0.48537911]
(2,)
[-0.00290927]
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(2,)
[ 0.31398823  0.49327764]
(2,)
[ 2.78533429]
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(2,)
[ 0.12022928  0.81725664]
(2,)
[ 0.5280407]
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[ 0.60773016]
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(2,)
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[ 0.28675026  0.61237736]
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[ 0.51383476  0.52301642]
(2,)
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(2,)
[ 0.50292602  0.64841422]
(2,)
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(2,)
[ 0.44065831  0.5932812 ]
(2,)
[ 0.11638369]
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(2,)
[ 0.4063392   0.51206394]
(2,)
[-1.01759309]
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(2,)
[ 0.26503604  0.58579983]
(2,)
[-0.18414518]
[ 0.43829925  0.68317799]
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(2,)
[ 0.43829925  0.68317799]
(2,)
Progress: 82.0% ... Training loss: 0.719 ... Validation loss: 1.222[ 1.26356707]
[ 0.2217361   0.79415561]
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(2,)
[ 0.2217361   0.79415561]
(2,)
[ 1.83684391]
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(2,)
[ 0.31796617  0.8463253 ]
(2,)
[-0.84183297]
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(2,)
[ 0.4174718   0.55455907]
(2,)
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(2,)
[ 0.42769609  0.57102703]
(2,)
[-1.13700243]
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(2,)
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(2,)
[-0.60191524]
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(2,)
[ 0.44130898  0.62353383]
(2,)
[ 1.2861199]
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(2,)
[ 0.71833466  0.47015512]
(2,)
[ 1.11173451]
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[ 0.58430919  0.25218931]
(2,)
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[ 0.59983833  0.50711366]
(2,)
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[ 0.30002061  0.62653855]
(2,)
[ 0.89683836]
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[ 0.28563526  0.74849101]
(2,)
[ 1.89415794]
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[ 0.13131375  0.8322069 ]
(2,)
[ 0.59758665]
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[ 0.5364196   0.44257952]
(2,)
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(2,)
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(2,)
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[ 0.35752578  0.48922469]
(2,)
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(2,)
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(2,)
[ 0.59954586  0.34389484]
(2,)
[ 1.34986951]
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(2,)
[ 0.33923863  0.73458215]
(2,)
[ 1.64749317]
[ 0.33552235  0.6448058 ]
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(2,)
[ 0.33552235  0.6448058 ]
(2,)
Progress: 83.0% ... Training loss: 0.717 ... Validation loss: 1.219[-0.58053491]
[ 0.35501585  0.51549428]
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[ 0.35501585  0.51549428]
(2,)
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[ 0.6707803   0.41190906]
(2,)
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(2,)
[ 0.6021556   0.33213993]
(2,)
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(2,)
[ 0.65235135  0.27823433]
(2,)
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(2,)
[ 0.71140263  0.40190585]
(2,)
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(2,)
[ 0.43813795  0.46765434]
(2,)
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(2,)
[ 0.67449389  0.34484737]
(2,)
[-0.40257167]
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(2,)
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(2,)
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[ 0.20385791  0.70184579]
(2,)
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[ 0.5855372   0.30727455]
(2,)
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[ 0.07806988  0.81296651]
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[ 0.09965337  0.85158006]
(2,)
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[ 0.24329456  0.69330852]
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(2,)
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(2,)
[-1.08202621]
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(2,)
[ 0.35880715  0.65932728]
(2,)
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(2,)
[ 0.79961234  0.34350509]
(2,)
Progress: 84.0% ... Training loss: 0.716 ... Validation loss: 1.220[-0.4661848]
[ 0.74923961  0.3182172 ]
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(2,)
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(2,)
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(2,)
[ 0.6969786   0.32881295]
(2,)
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[ 0.41735168  0.41571411]
(2,)
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(2,)
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[ 0.36143934  0.6824209 ]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.2677708   0.82355242]
(2,)
[ 0.23138181]
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[ 0.77394911  0.45463327]
(2,)
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(2,)
[ 0.13471432  0.79466468]
(2,)
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(2,)
[-0.12629664]
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(2,)
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(2,)
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(2,)
[ 2.22360432]
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(2,)
[ 0.26594008  0.86382335]
(2,)
[-0.82811455]
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(2,)
[ 0.60551441  0.51929756]
(2,)
Progress: 85.0% ... Training loss: 0.715 ... Validation loss: 1.223[-0.52620332]
[ 0.59624508  0.32638884]
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.38030543  0.63187475]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.56701659  0.42860488]
(2,)
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[ 1.67665659]
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[ 0.42582819  0.51113268]
(2,)
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[ 0.55489337  0.54750494]
(2,)
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[ 0.37403633  0.6813411 ]
(2,)
[ 1.75034626]
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[ 0.29852124  0.58033383]
(2,)
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[ 0.23465466  0.6955408 ]
(2,)
[ 0.06174281]
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[ 0.79147355  0.32356979]
(2,)
[-0.12939416]
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(2,)
[ 0.470327    0.51544367]
(2,)
[-0.05151185]
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(2,)
[ 0.75079776  0.17874782]
(2,)
Progress: 86.0% ... Training loss: 0.714 ... Validation loss: 1.224[ 0.06006597]
[ 0.26685211  0.6121098 ]
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[ 0.26685211  0.6121098 ]
(2,)
[ 1.01385944]
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(2,)
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(2,)
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(2,)
[ 0.47883401  0.47689962]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.57521549  0.45965208]
(2,)
[ 0.00492722]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[-0.5832544]
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[ 0.30749196  0.66731169]
(2,)
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[ 0.50889381  0.68739825]
(2,)
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[ 0.45724523  0.39848416]
(2,)
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[ 0.43929236  0.59651569]
(2,)
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(2,)
[ 0.54068401  0.52672849]
(2,)
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(2,)
[ 0.67591164  0.45735729]
(2,)
[-0.05284166]
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(2,)
[ 0.50910884  0.3725578 ]
(2,)
Progress: 87.0% ... Training loss: 0.713 ... Validation loss: 1.222[-0.14299292]
[ 0.68318415  0.31476374]
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(2,)
[ 0.68318415  0.31476374]
(2,)
[-0.1291347]
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(2,)
[ 0.45234324  0.39776275]
(2,)
[ 0.03718387]
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(2,)
[ 0.5004411   0.53591723]
(2,)
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(2,)
[ 0.80992374  0.20258147]
(2,)
[-0.83663336]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.17587168  0.7163181 ]
(2,)
[-0.43369028]
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.78354399  0.29199133]
(2,)
[ 2.46070958]
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(2,)
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(2,)
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(2,)
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(2,)
[-0.1659749]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.15456545  0.85976472]
(2,)
[ 1.37015462]
[ 0.15337443  0.78662296]
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(2,)
[ 0.15337443  0.78662296]
(2,)
Progress: 88.0% ... Training loss: 0.710 ... Validation loss: 1.212[ 0.03350943]
[ 0.51735575  0.47390401]
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(2,)
[ 0.51735575  0.47390401]
(2,)
[ 1.07754005]
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[ 0.17943777  0.7520895 ]
(2,)
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(2,)
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(2,)
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(2,)
[ 0.30045861  0.60354476]
(2,)
[-0.41318609]
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(2,)
[ 0.1733878   0.68146454]
(2,)
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(2,)
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(2,)
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(2,)
[ 0.28369032  0.73945523]
(2,)
[ 1.89585777]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 1.02309847]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.46569084  0.57210801]
(2,)
Progress: 89.0% ... Training loss: 0.710 ... Validation loss: 1.209[ 1.28316555]
[ 0.25267177  0.66497777]
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(2,)
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[ 0.53246487  0.58584502]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.74848085  0.31952813]
(2,)
[ 2.04997585]
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(2,)
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(2,)
[ 0.49594492  0.55637623]
(2,)
[-0.63274443]
[ 0.6714126   0.41166709]
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(2,)
[ 0.6714126   0.41166709]
(2,)
Progress: 90.0% ... Training loss: 0.708 ... Validation loss: 1.213[ 0.46814825]
[ 0.41044188  0.57794264]
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(2,)
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(2,)
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(2,)
[ 0.09524282]
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(2,)
[ 0.14662964  0.73570102]
(2,)
[-1.10422927]
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(2,)
[ 0.42127065  0.62825036]
(2,)
Progress: 91.0% ... Training loss: 0.707 ... Validation loss: 1.211[ 1.65640958]
[ 0.2354603   0.86165346]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.61887863]
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(2,)
[ 0.2049641   0.70644143]
(2,)
[-0.19605758]
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(2,)
[ 0.82158041  0.25607928]
(2,)
Progress: 92.0% ... Training loss: 0.705 ... Validation loss: 1.203[ 0.01086108]
[ 0.30085566  0.76379214]
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(2,)
[ 0.30085566  0.76379214]
(2,)
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[ 0.24490445  0.75187707]
(2,)
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(2,)
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(2,)
[ 0.30753774  0.5273136 ]
(2,)
[-0.61171702]
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[ 0.19665157  0.74707175]
(2,)
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(2,)
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[ 0.09835007  0.75720888]
(2,)
[-0.44791417]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.65885596  0.35642832]
(2,)
[ 3.3307137]
[ 0.46843536  0.61656433]
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(2,)
[ 0.46843536  0.61656433]
(2,)
Progress: 93.0% ... Training loss: 0.704 ... Validation loss: 1.200[-0.57220315]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.25352589  0.74751005]
(2,)
[ 0.88192501]
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[ 0.34883632  0.6522639 ]
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[ 0.3395268   0.44128397]
(2,)
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[ 0.69993944  0.37906283]
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[ 0.27487836  0.73605727]
(2,)
[ 1.051273]
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(2,)
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[ 0.56513975  0.57489353]
(2,)
[-0.23176119]
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(2,)
[ 0.46330486  0.45748474]
(2,)
[-0.66966726]
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(2,)
[ 0.48060291  0.56250193]
(2,)
Progress: 94.0% ... Training loss: 0.703 ... Validation loss: 1.199[ 0.03608571]
[ 0.54666034  0.498063  ]
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(2,)
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(2,)
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[ 0.5846512   0.46427955]
(2,)
[ 1.20124592]
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(2,)
[ 0.44670395  0.57111108]
(2,)
[ 1.53957128]
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[ 0.21532765  0.8254708 ]
(2,)
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(2,)
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(2,)
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[ 0.60618951  0.44529568]
(2,)
[ 1.57713493]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.27063565  0.65218113]
(2,)
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(2,)
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[ 0.5678737   0.47300432]
(2,)
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(2,)
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[ 0.40570464  0.57011232]
(2,)
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[ 0.64888771  0.3467561 ]
(2,)
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[ 0.59984099  0.45909478]
(2,)
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[ 0.41638209  0.65098517]
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[ 0.47483873  0.47540845]
(2,)
[-0.22110568]
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[ 0.77200819  0.23799889]
(2,)
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[ 0.54302857  0.52473056]
(2,)
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[ 0.56923155  0.54938254]
(2,)
[ 0.12077756]
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[ 0.20171306  0.70671803]
(2,)
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(2,)
[ 0.39960584  0.58216393]
(2,)
[-0.22556407]
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(2,)
[ 0.59770354  0.20399534]
(2,)
[ 1.87295203]
[ 0.17450444  0.80195362]
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(2,)
[ 0.17450444  0.80195362]
(2,)
Progress: 95.0% ... Training loss: 0.701 ... Validation loss: 1.193[-0.38741506]
[ 0.51232722  0.45078343]
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(2,)
[ 0.51232722  0.45078343]
(2,)
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(2,)
[ 0.31805016  0.65954005]
(2,)
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(2,)
[ 0.65980841  0.43062947]
(2,)
[ 2.18481473]
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(2,)
[ 0.20082188  0.77642939]
(2,)
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(2,)
[ 0.37545373  0.64576799]
(2,)
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(2,)
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(2,)
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(2,)
[ 0.67759069  0.37862181]
(2,)
[ 0.05995195]
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(2,)
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(2,)
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[ 0.10290934  0.83117679]
(2,)
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(2,)
[-0.14163013]
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[ 0.19041916  0.7466258 ]
(2,)
[-0.0488158]
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(2,)
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[ 0.67535184  0.33085712]
(2,)
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(2,)
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(2,)
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[ 0.34821571  0.5964098 ]
(2,)
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(2,)
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[ 0.49297175  0.48119787]
(2,)
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[ 0.48239999  0.48480092]
(2,)
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[ 0.49300137  0.49044576]
(2,)
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[ 0.74408868  0.30311747]
(2,)
[ 1.0217837]
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[ 0.39987875  0.68402933]
(2,)
[ 1.31886701]
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(2,)
[ 0.56934851  0.5281712 ]
(2,)
[ 0.36691002]
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(2,)
[ 0.48125263  0.66638175]
(2,)
[ 1.32566145]
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(2,)
[ 0.12222646  0.7952843 ]
(2,)
Progress: 96.0% ... Training loss: 0.700 ... Validation loss: 1.189[ 0.12866745]
[ 0.53083298  0.49652307]
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(2,)
[ 0.53083298  0.49652307]
(2,)
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(2,)
[ 0.39432652  0.66335869]
(2,)
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(2,)
[ 0.78414821  0.3551447 ]
(2,)
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(2,)
[ 0.70638783  0.25547969]
(2,)
[-0.81664272]
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(2,)
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(2,)
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(2,)
[ 0.45197703  0.45773377]
(2,)
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(2,)
[ 0.72322876  0.37212439]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[-0.24995101]
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(2,)
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(2,)
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[ 0.68615734  0.4587594 ]
(2,)
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[ 0.46409824  0.50900526]
(2,)
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[ 0.27571197  0.79842558]
(2,)
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(2,)
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(2,)
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(2,)
[ 0.48148948  0.46202152]
(2,)
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(2,)
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(2,)
[ 0.34405005  0.57670998]
(2,)
[-0.13065534]
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(2,)
[ 0.34388729  0.64218257]
(2,)
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(2,)
[ 0.36841712  0.64662759]
(2,)
Progress: 97.0% ... Training loss: 0.699 ... Validation loss: 1.185[-0.01886963]
[ 0.17125862  0.87233979]
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(2,)
[ 0.17125862  0.87233979]
(2,)
[ 2.252765]
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[ 0.41061712  0.68225081]
(2,)
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[ 0.86891305  0.15835981]
(2,)
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(2,)
[ 0.51976161  0.46539584]
(2,)
[-0.0873922]
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(2,)
[ 0.22197011  0.8107529 ]
(2,)
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(2,)
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(2,)
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(2,)
[ 0.75281842  0.17985831]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.34281515  0.52879212]
(2,)
[ 2.68760882]
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(2,)
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(2,)
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(2,)
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(2,)
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(2,)
[ 1.54047519]
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(2,)
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(2,)
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(2,)
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(2,)
[ 0.81407967  0.23501077]
(2,)
[-0.7932165]
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(2,)
[ 0.59266173  0.46081765]
(2,)
Progress: 98.0% ... Training loss: 0.698 ... Validation loss: 1.186[-0.05148331]
[ 0.39284139  0.64595785]
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(2,)
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(2,)
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(2,)
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[ 0.56965914  0.39971677]
(2,)
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(2,)
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(2,)
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(2,)
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(2,)
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[ 0.39232626  0.60943324]
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[ 1.13092671]
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[ 0.38730953  0.59131094]
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[ 0.23476839  0.78710036]
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[ 0.1561836   0.76653215]
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[ 0.38870193  0.44180446]
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[ 0.73448516  0.29571035]
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[-0.53708512]
[ 0.62230149  0.46829611]
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(2,)
[ 0.62230149  0.46829611]
(2,)
[-0.78093806]
[ 0.66551402  0.55541715]
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(2,)
[ 0.66551402  0.55541715]
(2,)
Progress: 99.0% ... Training loss: 0.697 ... Validation loss: 1.185
In [168]:
plt.plot(losses['train'], label='Training loss')
plt.plot(losses['validation'], label='Validation loss')
plt.legend()
_ = plt.ylim()

Check out your predictions

Here, use the test data to view how well your network is modeling the data. If something is completely wrong here, make sure each step in your network is implemented correctly.

In [178]:
fig, ax = plt.subplots(figsize=(8,4))

mean, std = scaled_features['cnt']
predictions = network.run(test_features).T*std + mean
ax.plot(predictions[0], label='Prediction')
ax.plot((test_targets['cnt']*std + mean).values, label='Data')
ax.set_xlim(right=len(predictions))
ax.legend()

dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])
dates = dates.apply(lambda d: d.strftime('%b %d'))
ax.set_xticks(np.arange(len(dates))[12::24])
_ = ax.set_xticklabels(dates[12::24], rotation=45)

OPTIONAL: Thinking about your results(this question will not be evaluated in the rubric).

Answer these questions about your results. How well does the model predict the data? Where does it fail? Why does it fail where it does?

Note: You can edit the text in this cell by double clicking on it. When you want to render the text, press control + enter

Your answer below